1. Field of the Invention
Embodiments of the present invention relate to the field of computerized tomography (CT) and, more particularly, to a method for sorting CT image slices and a method for constructing a 3-dimensional (3D) CT image using the sorted CT image slices.
2. Description of the Prior Art
Patient's respiratory motion during free-breathing CT scan may cause significant distortions in target contouring of tumors in the 3D image of the thorax and the upper abdomen. In order to eliminate or reduce the effect caused by respiratory motion generated artifacts on the CT scan of the chest and abdomen of the patient, achieving the purpose of accurate diagnosis and treatment, the concept of four-dimensional (4D) CT has been proposed. The 4D CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. 4D CT can be accomplished by over-sampling CT slices at each couch (Z direction) position, and then sorting all images (slices) into multiple CT volumes corresponding to different respiratory states. Each CT series (volume) is a 3D image of a specific respiratory state, and each 3D image is formed by overlapped slices chosen from different couches (one slice in one couch position). FIG. 1 schematically illustrates this sorting mechanism.
Currently two types of 4D CT methods are researched: one is an external Device-based 4D CT, and the other is a Device-less 4D CT method. The external device-based method is represented by Advantage 4D (A4D) CT which utilizes external respiratory signals, and the device-less method is represented by 4D CT sorting based on the patient's internal anatomy (Device-less 4D, i.e., D4D), with the respiratory signals thereof being extracted from the patient's internal image features.
A4D CT has been widely used for eliminating respiratory motion. This method requires the use of a Real-time Position Management (RPM) device (which includes complicated hardware and software) as an external device to monitor the respiratory motion of a patient. The general process of A4D CT is as follows: monitoring the respiratory motion of a patient using a respiration monitoring system connected to the CT device during the process of image collection, collecting CT images and respiratory signals simultaneously, “stamping” each layer of the collected CT images with time information indicative of its phase during the breathing circle (i.e., respiratory phase), sorting all the CT images according to the respiratory phase, and performing a 3D reconstruction based on the sorted images, wherein the 3D images for each of the respiratory phases constitute a 3D image sequence varying over time, hence referred to as 4D CT.
Current A4D CT systems mostly use a spirometer to measure the respiratory capacity of a patient, or an infrared camera device to measure the amplitude difference of movement of the patient's body surface during breathing, or a pressure sensor or the like to measure the pressure difference caused by breathing, and then convert the measured signals into breathing cycle signals. In such systems, CT images are generally collected in cinema mode (CINE mode) in the following manner: CT image collection is performed continuously in a certain period of time at a couch position. After a CINE mode scan at one couch position is completed, CT scan is then performed at the next couch position to repeat the same CINE mode scan. The whole process repeats until the entire desired scan scope is scanned.
In recent years, some D4D CT methods have been disclosed. A most representative one of these methods is proposed by Ruijiang Li, et al. in their paper “4D CT sorting based on patient internal anatomy”, PHYSICS IN MEDICINE AND BIOLOGY, 54 (2009) 4821-4833. In that paper, Li, et al. proposed a sorting method based on D4D CT, which is incorporated herein in its entirety for reference. In the method, four internal anatomy features (including the air content, lung area, lung density and body area) are introduced, and a measure called spatial coherence is used to select the optimal internal feature at each couch position and to generate the respiratory curves for 4D CT sorting based on the selected optimal internal feature. The method eliminates the use of an external instrument for recording respiratory motion synchronously and can be implemented in medical devices while reducing cost.
Existing 4D CT techniques are also described in US patent applications US20090225957, US20100202673, US20070286331, etc.
An important parameter for 4D CT is the sampling rate. A denser sampling means collecting more CT slices during one breathing cycle, which is helpful to improve 4D sorting accuracy and decrease the image mismatch between two adjacent Z (couch) positions. However, in most clinical 4D CT scans, there are always some reasons which prevent the sampling rate from being set too dense, such as the requirement for reducing X-ray dose to the patient, the limitation of the total CT slice number and the storage ability, or the requirement for improving scan and processing speed. Therefore, 4D CT slices are mostly generated by sparse sampling, and mismatches along the Z direction in the 3D images often occur, which can lead to a certain degree of tumor contour defect or organ surface split, as shown by circles in FIGS. 2A and 2B.
There are two aspects about the impacts caused by low sampling rate. One is the insufficiency of sampling points in the whole breathing cycle. For example, assume there are less than 10 sampling points in one breathing cycle. If we plot 10 phases for one cycle, then there must be at least one point used as two phases. Consequently, about 10% mismatches are caused by insufficient sampling rate. The second aspect may cause even more serious mismatch. It is found that in different phases of one breathing cycle, the moving speeds of the body or organ are different; especially in the middle phase of expiration, a small difference in phase corresponds to a bigger organ (body) movement. Thus, even if the sampling rate is high for the whole breathing cycle, it may still be relatively sparse for the special fast moving phase, as shown by the example in FIG. 3. In FIG. 3, the curve represents the respiratory motion curve of the patient, and the elliptic points represent the breathing data extracted from each image slice at one couch position, wherein the Y-axis represents the breathing feature value (namely, the amplitude of the body or organ movement during the patient's breathing) extracted from the image slices, and the X-axis represents the time at which the image slice is scanned (which corresponds to the respiratory phase). As can be seen from FIG. 3, the breathing feature values change dramatically in the middle of the expiration phase (as shown by the red circle), and sampling rate is especially insufficient for these parts. By checking the 3D CT images, it is discovered that 3D mismatch mainly occurs in these situations.
As shown in FIG. 3, a small phase error (3% difference) causes a big amplitude difference (31% difference). Therefore, for two adjacent couch (Z) CT slices, when they are stacked to a 3D image, if there is a small phase shift between the two samples, then the two images chosen may have obviously different appearance, thereby causing an obvious mismatch along the Z direction in the resulting 3D image.